GAD65 antigen therapy in recently diagnosed type 1 diabetes mellitus.

BACKGROUND: The 65-kD isoform of glutamic acid decarboxylase (GAD65) is a major autoantigen in type 1 diabetes. We hypothesized that alum-formulated GAD65 (GAD-alum) can preserve beta-cell function in patients with recent-onset type 1 diabetes. METHODS: We studied 334 patients, 10 to 20 years of age, with type 1 diabetes, fasting C-peptide levels of more than 0.3 ng per milliliter (0.1 nmol per liter), and detectable serum GAD65 autoantibodies. Within 3 months after diagnosis, patients were randomly assigned to receive one of three study treatments: four doses of GAD-alum, two doses of GAD-alum followed by two doses of placebo, or four doses of placebo. The primary outcome was the change in the stimulated serum C-peptide level (after a mixed-meal tolerance test) between the baseline visit and the 15-month visit. Secondary outcomes included the glycated hemoglobin level, mean daily insulin dose, rate of hypoglycemia, and fasting and maximum stimulated C-peptide levels. RESULTS: The stimulated C-peptide level declined to a similar degree in all study groups, and the primary outcome at 15 months did not differ significantly between the combined active-drug groups and the placebo group (P=0.10). The use of GAD-alum as compared with placebo did not affect the insulin dose, glycated hemoglobin level, or hypoglycemia rate. Adverse events were infrequent and mild in the three groups, with no significant differences. CONCLUSIONS: Treatment with GAD-alum did not significantly reduce the loss of stimulated C peptide or improve clinical outcomes over a 15-month period. (Funded by Diamyd Medical and the Swedish Child Diabetes Foundation; ClinicalTrials.gov number, NCT00723411.).

HEIDA: Software Examples for Rapid Introduction of Homomorphic Encryption for Privacy Preservation of Health Data.

Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.